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2000
Volume 20, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603
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Abstract

The most common primary malignant brain tumor is glioblastoma. Glioblastoma Multiforme (GBM) diagnosis is difficult. However, image segmentation and registration methods may simplify and automate Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) scan analysis. Medical practitioners and researchers can better identify and characterize glioblastoma tumors using this technology. Many segmentation and registration approaches have been proposed recently. Note that these approaches are not fully compiled. This review efficiently and critically evaluates the state-of-the-art segmentation and registration techniques for MRI and CT GBM images, providing researchers, medical professionals, and students with a wealth of knowledge to advance GBM imaging and inform decision-making. GBM's origins and development have been examined, along with medical imaging methods used to diagnose tumors. Image segmentation and registration were examined, showing their importance in this difficult task. Frequently encountered glioblastoma segmentation and registration issues were examined. Based on these theoretical foundations, recent image segmentation and registration advances were critically analyzed. Additionally, evaluation measures for analytical efforts were thoroughly reviewed.

© 2024 The Author(s). Published by Bentham Science Publisher.
This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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2024-01-01
2025-07-26

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References

  1. OstromQ.T. GittlemanH. TruittG. BosciaA. KruchkoC. Barnholtz-SloanJ.S. CBTRUS statistical report: Primary brain and other central nervous system tumors diagnosed in the United States in 2011–2015.Neuro-oncol.201820iv1iv8610.1093/neuonc/noy131
     [Google Scholar]
  2. BiserovaK. JakovlevsA. UljanovsR. StrumfaI. Cancer stem cells: Significance in origin, pathogenesis and treatment of glioblastoma.Cells20211062110.3390/cells10030621
     [Google Scholar]
  3. SanaiN. Alvarez-BuyllaA. BergerM.S. Neural stem cells and the origin of gliomas.N. Engl. J. Med.2005353811822
     [Google Scholar]
  4. CantrellJ.N. WaddleM.R. RotmanM. PetersonJ.L. Ruiz-GarciaH. HeckmanM.G. Quiñones-HinojosaA. RosenfeldS.S. BrownP.D. TrifilettiD.M. Progress toward long-term survivors of glioblastoma.Mayo Clin. Proc.2019941278128610.1016/j.mayocp.2018.11.031
     [Google Scholar]
  5. CarranoA. JuarezJ.J. IncontriD. IbarraA. Guerrero CazaresH. Sex-specific differences in glioblastoma.Cells202110178310.3390/cells10071783
     [Google Scholar]
  6. LiR. ChenX. YouY. WangX. LiuY. HuQ. YanW. Comprehensive portrait of recurrent glioblastoma multiforme in molecular and clinical characteristics.Oncotarget2015630968
     [Google Scholar]
  7. PanI-W. FergusonS.D. LamS. Patient and treatment factors associated with survival among adult glioblastoma patients: a USA population-based study from 2000–2010.J. Clin. Neurosci.20152215751581
     [Google Scholar]
  8. AlexanderB.M. CloughesyT.F. Adult Glioblastoma.J. Clin. Oncol.2017352402240910.1200/jco.2017.73.0119
     [Google Scholar]
  9. ShergalisA. BankheadA. LuesakulU. MuangsinN. NeamatiN. Current challenges and opportunities in treating glioblastoma.Pharmacol. Rev.201870412445
     [Google Scholar]
  10. Cruz Da SilvaE. MercierM-C. Etienne-SelloumN. DontenwillM. ChoulierL. A systematic review of glioblastoma-targeted therapies in phases II, III, IV clinical trials.Cancers (Basel)202113179510.3390/cancers13081795
     [Google Scholar]
  11. MaJ. LiT. ChenH. WangC. WangH. LiQ. Lipidomic analysis and diagnosis of glioblastoma multiforme with rapid evaporative ionization mass spectrometry.Electrophoresis2021421965197310.1002/elps.202100130
     [Google Scholar]
  12. WrightC.H. WrightJ. OnyewadumeL. RaghavanA. LapiteI. Casco-ZuletaA. LagmanC. SajatovicM. HodgesT.R. Diagnosis, treatment, and survival in spinal dissemination of primary intracranial glioblastoma: systematic literature review.J. Neurosurg. Spine20193172373210.3171/2019.5.SPINE19164
     [Google Scholar]
  13. SchebeschK-M. RosengarthK. BrawanskiA. ProescholdtM. WendlC. HöhneJ. OttC. LameckerH. DoenitzC. Clinical benefits of combining different visualization modalities in neurosurgery.Front. Surg.2019610.3389/fsurg.2019.00056
     [Google Scholar]
  14. Corroyer-DulmontA. ChakhoyanA. ColletS. DurandL. MacKenzieE.T. PetitE. BernaudinM. TouzaniO. ValableS. Imaging modalities to assess oxygen status in glioblastoma.Front. Med. (Lausanne)201525710.3389/fmed.2015.00057
     [Google Scholar]
  15. ScarabinoT. PopolizioT. TrojsiF. GiannatempoG. PolliceS. MaggialettiN. CarrieroA. Di CostanzoA. TedeschiG. SalvoliniU. Role of advanced MR imaging modalities in diagnosing cerebral gliomas.Radiol. Med. (Torino)200911444846010.1007/s11547‑008‑0351‑9
     [Google Scholar]
  16. HaydarN. AlyousefK. AlananU. IssaR. BaddourF. Al-shehabiZ. Al-janabiM.H. Role of Magnetic Resonance Imaging (MRI) in grading gliomas comparable with pathology: A cross-sectional study from Syria.Ann. Med. Surg. (Lond.)20228210.1016/j.amsu.2022.104679
     [Google Scholar]
  17. ShiroishiM.S. BoxermanJ.L. PopeW.B. Physiologic MRI for assessment of response to therapy and prognosis in glioblastoma.Neuro-oncol.20161846747810.1093/neuonc/nov179
     [Google Scholar]
  18. GilardV. TebaniA. DabajI. LaquerrièreA. FontanillesM. DerreyS. MarretS. BekriS. Diagnosis and management of glioblastoma: A comprehensive perspective.J. Pers. Med.20211125810.3390/jpm11040258
     [Google Scholar]
  19. KozlovaE.I. Narrow-profile standards: Pros and cons.Bibliotekovedenie202068576582[Russian Journal of Library Science].10.25281/0869‑608X‑2019‑68‑6‑576‑582
     [Google Scholar]
  20. YangD. Standardized MRI assessment of high-grade glioma response: a review of the essential elements and pitfalls of the RANO criteria.Neurooncol. Pract.20163596710.1093/nop/npv023
     [Google Scholar]
  21. DumbaM. FryA. SheltonJ. BoothT.C. JonesB. ShuaibH. WilliamsM. Imaging in patients with glioblastoma: A national cohort study.Neurooncol. Pract.2022948749510.1093/nop/npac048
     [Google Scholar]
  22. SinghH. MauryaV. GillS. Computerised tomography features in gliomas.Med. J. Armed Forces India20025822122510.1016/S0377‑1237(02)80134‑4
     [Google Scholar]
  23. OkorieA. Image Segmentation-Registration Cooperative Techniques Applied to Biomedicine and Remote Sensing.Delaware State University2022
     [Google Scholar]
  24. D. NARAIN PONRAJ M. EVANGELIN JENIFER, P. Poongodi, Comparative analysis of watershed segmentation technique and conventional Segmentation Techniques of Mammogram.International Journal of Imaging and Robotics20139106115
     [Google Scholar]
  25. SharmaA. A research review on segmentation techniques.Int J Res Appl Sci Eng Technol V20171170–117310.22214/ijraset.2017.8165
     [Google Scholar]
  26. MunawwarS. NagamaniT. Sudhakar ReddyG. Segmentation Techniques in Image Processing2018www.jetir.org
     [Google Scholar]
  27. KrishnaveniA. ShankarR. DuraisamyS. Swarm intelligence algorithms with k-means clustering for mammogram image segmentation, IRJMETS.ISSNn.d.25158260
     [Google Scholar]
  28. LyuH. FuH. HuX. LiuL. Esnet: Edge-based segmentation network for real-time semantic segmentation in traffic scenes2019 IEEE International Conference on Image Processing (ICIP)IEEE201918551859
     [Google Scholar]
  29. KaurD. KaurY. Various image segmentation techniques: a review.International Journal of Computer Science and Mobile Computing20143809814
     [Google Scholar]
  30. GuoY. LiuY. GeorgiouT. LewM.S. A review of semantic segmentation using deep neural networks.Int. J. Multimed. Inf. Retr.20187879310.1007/s13735‑017‑0141‑z
     [Google Scholar]
  31. HafizA.M. BhatG.M. A survey on instance segmentation: state of the art.Int. J. Multimed. Inf. Retr.2020917118910.1007/s13735‑020‑00195‑x
     [Google Scholar]
  32. HaoS. ZhouY. GuoY. A brief survey on semantic segmentation with deep learning.Neurocomputing202040630232110.1016/j.neucom.2019.11.118
     [Google Scholar]
  33. ChengB. CollinsM.D. ZhuY. LiuT. HuangT.S. AdamH. ChenL-C. Panoptic-DeepLab: A simple, strong, and fast baseline for bottom-up panoptic segmentation2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)IEEE20201247212482
     [Google Scholar]
  34. ChenL-C. PapandreouG. KokkinosI. MurphyK. YuilleA.L. DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs.IEEE Trans. Pattern Anal. Mach. Intell.20184083484810.1109/TPAMI.2017.2699184
     [Google Scholar]
  35. AlmotairiS. KareemG. AoufM. AlmutairiB. SalemM.A-M. Liver Tumor Segmentation in CT Scans Using Modified SegNet.Sensors (Basel)2020205151610.3390/s20051516
     [Google Scholar]
  36. CaiL. GaoJ. ZhaoD. A review of the application of deep learning in medical image classification and segmentation.Ann. Transl. Med.2020871371310.21037/atm.2020.02.44
     [Google Scholar]
  37. HoeserT. KuenzerC. Object detection and image segmentation with deep learning on earth observation data: A review-part i: Evolution and recent trends.Remote Sens. (Basel)2020121667
     [Google Scholar]
  38. SiddiqueN. PahedingS. ElkinC.P. DevabhaktuniV. U-net and its variants for medical image segmentation: A review of theory and applications.IEEE Access202198203182057
     [Google Scholar]
  39. CuiS. WeiM. LiuC. JiangJ. GAN‐segNet: A deep generative adversarial segmentation network for brain tumor semantic segmentation.Int. J. Imaging Syst. Technol.20223285786810.1002/ima.22677
     [Google Scholar]
  40. Lo VercioL. AmadorK. BannisterJ.J. CritesS. GutierrezA. MacDonaldM.E. MooreJ. MouchesP. RajashekarD. SchimertS. Supervised machine learning tools: a tutorial for clinicians.J. Neural Eng.202017062001
     [Google Scholar]
  41. SeoH. Badiei KhuzaniM. VasudevanV. HuangC. RenH. XiaoR. JiaX. XingL. Machine learning techniques for biomedical image segmentation: an overview of technical aspects and introduction to state‐of‐art applications.Med. Phys.202047e148e167
     [Google Scholar]
  42. RayS. A quick review of machine learning algorithms2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon)IEEE20193539
     [Google Scholar]
  43. de los ReyesA.M. BuemiM.E. AlemánM.N. SuárezC. Development of a graphic interface for the three-dimensional semiautomatic glioblastoma segmentation based on magnetic resonance images, in: 2018 Congreso Argentino de Ciencias de La Informática y Desarrollos de Investigación (CACIDI).IEEE201816
     [Google Scholar]
  44. FyllingenE.H. StensjøenA.L. BerntsenE.M. SolheimO. ReinertsenI. Glioblastoma segmentation: Comparison of three different software packages.PLoS One201611e016489110.1371/journal.pone.0164891
     [Google Scholar]
  45. WangH. HuJ. SongY. ZhangL. BaiS. YiZ. Multi-view fusion segmentation for brain glioma on CT images.Appl. Intell.2022115
     [Google Scholar]
  46. AliS. LiJ. PeiY. KhurramR. K. ur Rehman, T. Mahmood, A comprehensive survey on brain tumor diagnosis using deep learning and emerging hybrid techniques with multi-modal MR image.Arch. Comput. Methods Eng.2022294871489610.1007/s11831‑022‑09758‑z
     [Google Scholar]
  47. DongW. SunS. YinM. A multi-view deep learning model for pathology image diagnosis.Appl. Intell.2023537186720010.1007/s10489‑022‑03918‑1
     [Google Scholar]
  48. ChenL. LiuQ. LiuK. LuJ. SongL. YangK. Glioma Image Segmentation Method on Fully Convolutional Neural NetworkProceedings of the 6th International Conference on Biomedical Signal and Image ProcessingACMNew York, NY, USA20214653
     [Google Scholar]
  49. KalantarR. LinG. WinfieldJ.M. MessiouC. LalondrelleS. BlackledgeM.D. KohD-M. Automatic segmentation of pelvic cancers using deep learning: state-of-the-art approaches and challenges.Diagnostics (Basel)2021111964
     [Google Scholar]
  50. LiuX. SongL. LiuS. ZhangY. A review of deep-learning-based medical image segmentation methods.Sustainability2021131224
     [Google Scholar]
  51. RueckertD. Nonrigid registration: Concepts, algorithms, and applications.Medical Image Registration2001281301
     [Google Scholar]
  52. RahunathanS. StredneyD. SchmalbrockP. ClymerB.D. Image registration using rigid registration and maximization of mutual information13th Annu. Med. Meets Virtual Reality Conf2005
     [Google Scholar]
  53. LeuteneggerS. ChliM. SiegwartR.Y. BRISK: Binary robust invariant scalable keypoints2011 International Conference on Computer Vision, Ieee201125482555
     [Google Scholar]
  54. MujaM. LoweD.G. Fast matching of binary features2012 Ninth Conference on Computer and Robot VisionIEEE2012404410
     [Google Scholar]
  55. ShiriI. HajianfarG. SohrabiA. AbdollahiH. ShayestehS.P. GeramifarP. ZaidiH. OveisiM. RahmimA. Repeatability of radiomic features in magnetic resonance imaging of glioblastoma: Test–retest and image registration analyses.Med. Phys.20204742654280
     [Google Scholar]
  56. VisserM. PetrJ. MüllerD.M.J. EijgelaarR.S. HendriksE.J. WitteM. BarkhofF. Van HerkM. MutsaertsH.J.M.M. VrenkenH. Accurate MR image registration to anatomical reference space for diffuse glioma.Front. Neurosci.202014585
     [Google Scholar]
  57. MokT.C.W. ChungA.C.S. Robust image registration with absent correspondences in pre-operative and follow-up brain mri scans of diffuse glioma patients.International MICCAI Brainlesion Workshop.Springer2022231240
     [Google Scholar]
  58. RafiA. AliJ. AkramT. FiazK. Raza ShahidA. RazaB. Mustafa MadniT. U-Net based glioblastoma segmentation with patient’s overall survival prediction.2020223210.1007/978‑3‑030‑43364‑2_3
     [Google Scholar]
  59. KurmukovA. DalechinaA. SaparovT. BelyaevM. ZolotovaS. GolanovA. NikolaevaA. Challenges in building of deep learning models for glioblastoma segmentation: Evidence from clinical data. InPublic Health and Informatics2021298302
     [Google Scholar]
  60. Holtzman GazitM. FaranR. StepovoyK. PelesO. ShamirR.R. Post-operative glioblastoma multiforme segmentation with uncertainty estimation.Front. Hum. Neurosci.20221693244110.3389/fnhum.2022.932441
     [Google Scholar]
  61. LiC. HuangW. ChenX. WeiY. PriceS.J. SchönliebC-B. Expectation-maximization regularized deep learning for weakly supervised tumor segmentation for glioblastoma.2021
  62. SahliH. Ben SlamaA. ZeraiiA. LabidiS. SayadiM. ResNet-SVM: Fusion based glioblastoma tumor segmentation and classification.J. XRay Sci. Technol.202331274810.3233/XST‑221240
     [Google Scholar]
  63. PreciousJ.G. KirubhaS.P.A. PremkumarR. EvangelineI.K. Automatic 2D and 3D segmentation of glioblastoma brain tumor.Biomed Eng (Singapore)2023352225005510.4015/S1016237222500557
     [Google Scholar]
  64. ShaukatZ. Q. ul A. Farooq, S. Tu, C. Xiao, S. Ali, A state-of-the-art technique to perform cloud-based semantic segmentation using deep learning 3D U-Net architecture.BMC Bioinformatics20222325110.1186/s12859‑022‑04794‑9
     [Google Scholar]
  65. HeK. ZhaoW. XieX. JiW. LiuM. TangZ. ShiY. ShiF. GaoY. LiuJ. ZhangJ. ShenD. Synergistic learning of lung lobe segmentation and hierarchical multi-instance classification for automated severity assessment of COVID-19 in CT images.Pattern Recognit.202111310782810.1016/j.patcog.2021.107828
     [Google Scholar]
  66. NaiY-H. TeoB.W. TanN.L. O’DohertyS. StephensonM.C. ThianY.L. ChiongE. ReilhacA. Comparison of metrics for the evaluation of medical segmentations using prostate MRI dataset.Comput. Biol. Med.2021134104497
     [Google Scholar]
  67. SunJ. PengY. GuoY. LiD. Segmentation of the multimodal brain tumor image used the multi-pathway architecture method based on 3D FCN.Neurocomputing20214233445
     [Google Scholar]
  68. YeapP.L. WongY.M. OngA.L.K. TuanJ.K.L. PangE.P.P. ParkS.Y. LeeJ.C.L. TanH.Q. Predicting dice similarity coefficient of deformably registered contours using Siamese neural network.Phys. Med. Biol.202368155016
     [Google Scholar]
  69. CelayaA. RivièreB.M. FuentesD.T. A generalized surface loss for reducing the hausdorff distance in medical imaging segmentation2023https://api.semanticscholar.org/CorpusID:256662295
     [Google Scholar]
  70. RainaV. MolchanovaN. GrazianiM. MalininA. MullerH. CuadraM.B. GalesM. Tackling bias in the dice similarity coefficient: Introducing nDSC for white matter lesion segmentation2023
  71. KarimiD. SalcudeanS.E. Reducing the hausdorff distance in medical image segmentation with convolutional neural networks.IEEE Trans Med Imaging 39202049951310.1109/TMI.2019.2930068
     [Google Scholar]
  72. YangB. ChenX. LiJ. ZhuJ. MenK. DaiJ. A feasible method to evaluate deformable image registration with deep learning–based segmentation.Phys. Med.202295505610.1016/j.ejmp.2022.01.006
     [Google Scholar]
  73. NavarroF. Escudero-VinoloM. BescosJ. Accurate segmentation and registration of skin lesion images to evaluate lesion change.IEEE J. Biomed. Health Inform.20192350150810.1109/JBHI.2018.2825251
     [Google Scholar]
  74. ZouW. LuoY. CaoW. HeZ. HeZ. A cascaded registration network RCINet with segmentation mask.Neural Comput. Appl.202133164711648710.1007/s00521‑021‑06243‑9
     [Google Scholar]
  75. ChenS. LiD. FengC. Cardiac segmentation using multi-atlas coarse-refine registrationThe Fourth International Symposium on Image Computing and Digital MedicineACMNew York, NY, USA2020117120
     [Google Scholar]
  76. LiuL. Avilés-RiveroA.I. SchönliebC-B. Contrastive registration for unsupervised medical image segmentation.IEEE Trans. Neural Netw. Learn. Syst.2023
     [Google Scholar]
  77. WuL. WangJ. LiuX-J. A modified protocol to evaluate masseter morphology based on automatic segmentation and registration2022 International Conference on Service Robotics (ICoSR)IEEE20228590
     [Google Scholar]
/content/journals/cmir/10.2174/0115734056309829240909095801
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A Comprehensive Review of the Recent Advancements in Imaging Segmentation and Registration Techniques for Glioblastoma and Focusing on the Utilization of Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) Scans
Curr. Med. Imaging 20, e15734056309829 (2024); https://doi.org/10.2174/0115734056309829240909095801
/content/journals/cmir/10.2174/0115734056309829240909095801
/content/journals/cmir/10.2174/0115734056309829240909095801
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  • Article Type:     Review Article
Keyword(s): CT; Glioblastoma; Image registration; Image segmentation; Malignant brain tumor; MRI

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